What's Changed
- Codebase Migration: Consolidated tpot2 into tpot; removed deprecated/experimental features.
- Performance Enhancements: Optimized pipeline evaluation processes and genetic programming operators, leading to faster convergence and reduced computational overhead.
- Graph-Based Pipelines: Introduced a flexible graph-based representation of machine learning pipelines, enhancing the exploration of complex model architectures.
- Dependency Updates: Updated dependencies to ensure compatibility with the latest versions of scikit-learn and other essential libraries.
- Stability Improvements: Resolved various bugs and improved error handling to enhance overall stability and user experience.
- Genetic Feature Selection: Implemented genetic feature selection mechanisms, enabling automatic identification of relevant features during pipeline optimization.
- Expanded Search Spaces: Enhanced the flexibility in defining search spaces, allowing for more comprehensive exploration of potential pipeline configurations.
- Modular Framework: Refactored the codebase into a more modular structure, simplifying customization and extension of the evolutionary algorithm components.
- Documentation Overhaul: Revised and expanded documentation, including updated examples and comprehensive guides to reflect the new features and API changes.
Key Contributors
- Pedro Henrique Ribeiro (Lead developer - https://github.com/perib, https://www.linkedin.com/in/pedro-ribeiro/)
- Anil Saini (anil.saini@cshs.org)
- Jose Hernandez (jgh9094@gmail.com)
- Jay Moran (jay.moran@cshs.org)
- Nicholas Matsumoto (nicholas.matsumoto@cshs.org)
- Gabriel Ketron (Gabriel.Ketron@cshs.org)
- Hyunjun Choi (hyunjun.choi@cshs.org)
- Miguel E. Hernandez (miguel.e.hernandez@cshs.org)
- Jason Moore (moorejh28@gmail.com)
Full Changelog: https://github.com/EpistasisLab/tpot/commits/v1.0.0